Selective Detection of Hydrocarbons in Real Atmospheric Conditions by Single MOX Sensor in Temperature Modulation Mode ^†

Abstract

Selective detection of hydrocarbons – methane and propane – in urban air for industrial safety properties by single metal oxide semiconductor gas sensor has been demonstrated. As sensors were fabricated on the basis of nanocrystalline SnO₂ and alumina micro-hotplates. Sensor working temperature modulation has been applied during raw sensor data collection. Pre-processing of acquired data – scaling, baseline extraction and exclusion of non-valid data points has been demonstrated to be critical procedures before application of machine learning algorithms. The achieved accuracy of 86% for correct gas identification in 40-200 ppm range has been demonstrated.

1. Introduction

Detection of pipeline hydrocarbons leakage is a valid industrial demand [1]. A deployed network of autonomous miniature micromachined metal oxide semiconductor gas sensors with low power consumption possess a great perspective of practical use in this regard [2,3]. The main obstacle of their high cross sensitivity can be overcome by the implementation of sensor arrays or working temperature modulation in combination of signal processing and nonlinear calibration [4,5]. In this work we demonstrate stable selective detection of propane and methane in low concentrations in the real urban ambient air by the SnO₂-based semiconductor gas sensor.

2. Experimental

Nanocrystalline SnO₂ gas sensitive material has been synthesized by flame spray pyrolysis technique. Gas sensors were fabricated on the basis of 2 × 2 × 0.15 mm alumina micro-hotplates with the use of α-terpineol as a binder. Measurements were carried out in a flow-through sensor cell with the use of outdoor air with the admixture of methane and propane from certified gas bottles. Gas concentrations varied from 40 to 200 ppm. Sets of data were collected during a series of 24h experiments with variable air temperature and humidity. The measurements were conducted through 2 consecutive months in order to determine the stability of sensor performance. Collected 17 data sets were divided to 10 sets, used for model training and calibration, and 7 sets, used for motel testing. The details of sensors working temperature cycle and gas sensor setup are given on Figure 1.

3. Results

The obtained gas sensor resistance profiles, recorded during temperature cycles, demonstrate considerable variance due to effects of ambient air humidity and temperature changes. The application of principal component analysis (PCA) to the raw sensor data did not allow to distinguish between methane, propane and air in any acceptable extent (Figure 2a). The use of data pre-processing, represented on Figure 2b (baseline cut-off, data scaling, extraction of data points only from 300–500 °C working temperature region), in combination with machine learning algorithm (artificial neural network with 50 neurons in hidden layer, dropout regularization and sigmoidal activation function) allowed to achieve 86% accuracy of identification of methane vs. propane vs. air in real urban air in 40–200 ppm concentration range.

4. Conclusions

Data preprocessing allows for compensation of metal oxide gas sensor drift effects during operation in real urban air, caused by variations of weather conditions. Application of machine learning algorithms, based on the artificial neural network approach gives the possibility of selective detection of air pollutants even of very close chemical nature. The presented approach demonstrates the applicability of MOX sensors for application in industrial safety tasks, related to flammable and explosive gases leakage.

Acknowledges

The work was funded by Russian Science Foundation grant № 17-73-10491.